source: TOOLS/MOSAIX/RunoffWeights.py @ 6173

Last change on this file since 6173 was 6105, checked in by omamce, 2 years ago

MOSAIX, by O.M : adapt to new file format of NEMO4.2

(no halo or peridodicity band in files)
  • Property svn:keywords set to Date Revision HeadURL Author Id
File size: 23.3 KB
Line 
1# -*- Mode: python -*-
2#!/usr/bin/env python3
3### ===========================================================================
4###
5### Compute runoff weights.
6### For LMDZ only. Not suitable for DYNAMICO
7###
8### ===========================================================================
9##
10##  Warning, to install, configure, run, use any of Olivier Marti's
11##  software or to read the associated documentation you'll need at least
12##  one (1) brain in a reasonably working order. Lack of this implement
13##  will void any warranties (either express or implied).
14##  O. Marti assumes no responsability for errors, omissions,
15##  data loss, or any other consequences caused directly or indirectly by
16##  the usage of his software by incorrectly or partially configured
17##  personal.
18##
19# SVN information
20__Author__   = "$Author$"
21__Date__     = "$Date$"
22__Revision__ = "$Revision$"
23__Id__       = "$Id$"
24__HeadURL__  = "$HeadURL$"
25__SVN_Date__ = "$SVN_Date: $"
26##
27
28## Modules
29import numpy as np, xarray as xr
30import nemo
31from scipy import ndimage
32import sys, os, platform, argparse, textwrap, time
33
34## Useful constants
35zero    = np.dtype('float64').type(0.0)
36zone    = np.dtype('float64').type(1.0)
37epsfrac = np.dtype('float64').type(1.0E-10)
38pi      = np.pi
39rad     = pi/np.dtype('float64').type(180.0)  # Conversion from degrees to radian
40ra      = np.dtype('float64').type(6371229.0) # Earth radius
41
42## Functions
43def geodist (plon1, plat1, plon2, plat2) :
44      """Distance between two points (on sphere)"""
45      zs = np.sin (rad*plat1) * np.sin (rad*plat2) +  np.cos (rad*plat1) * np.cos (rad*plat2) * np.cos(rad*(plon2-plon1))
46      zs = np.maximum (-zone, np.minimum (zone, zs))
47      geodist =  np.arccos (zs)
48      return geodist
49
50### ===== Reading command line parameters ==================================================
51# Creating a parser
52parser = argparse.ArgumentParser (
53    description = """Compute calving weights""",
54    epilog='-------- End of the help message --------')
55
56# Adding arguments
57parser.add_argument ('--oce'          , help='oce model name', type=str, default='eORCA1.2',
58                         choices=['ORCA2.3', 'eORCA1.2', 'eORCA1.4', 'eORCA1.4.2', 'eORCA025', 'eORCA025.1', 'eORCA025.4'] )
59parser.add_argument ('--atm'          , help='atm model name', type=str, default='LMD9695'    )
60parser.add_argument ('--atmCoastWidth', help='width of the coastal band in atmosphere (in grid points)', type=int, default=1 )
61parser.add_argument ('--oceCoastWidth', help='width of the coastal band in ocean (in grid points)'     , type=int, default=2 )
62parser.add_argument ('--atmQuantity'  , help='Quantity if atm provides quantities (m/s), Surfacic if atm provided flux (m/s/m2)' , type=str,
63                         default='Quantity', choices=['Quantity', 'Surfacic'] )
64parser.add_argument ('--oceQuantity'  , help='Quantity if oce requires quantities (ks/s), Surfacic if oce requires flux (m/s/m2)', type=str,
65                         default='Surfacic', choices=['Quantity', 'Surfacic'] )
66parser.add_argument ('--searchRadius' , help='max distance to connect a land point to an ocean point (in km)', type=float, default=600.0 )
67parser.add_argument ('--grids' , help='grids file name', default='grids.nc' )
68parser.add_argument ('--areas' , help='masks file name', default='areas.nc' )
69parser.add_argument ('--masks' , help='areas file name', default='masks.nc' )
70parser.add_argument ('--o2a'   , help='o2a file name'  , default='o2a.nc'   )
71parser.add_argument ('--output', help='output rmp file name', default='rmp_tlmd_to_torc_runoff.nc' )
72parser.add_argument ('--fmt'   , help='NetCDF file format, using nco syntax', default='netcdf4',
73                         choices=['classic', 'netcdf3', '64bit', '64bit_data', '64bit_data', 'netcdf4', 'netcdf4_classsic'] )
74parser.add_argument ('--ocePerio'   , help='periodicity of ocean grid', type=float, default=0, choices=nemo.nperio_valid_range)
75
76# Parse command line
77myargs = parser.parse_args()
78
79#
80grids = myargs.grids
81areas = myargs.areas
82masks = myargs.masks
83o2a   = myargs.o2a
84
85# Model Names
86atm_Name = myargs.atm
87oce_Name = myargs.oce
88# Width of the coastal band (land points) in the atmopshere
89atmCoastWidth = myargs.atmCoastWidth
90# Width of the coastal band (ocean points) in the ocean
91oceCoastWidth = myargs.oceCoastWidth
92searchRadius  = myargs.searchRadius * 1000.0 # From km to meters
93# Netcdf format
94if myargs.fmt == 'classic'         : FmtNetcdf = 'CLASSIC'
95if myargs.fmt == 'netcdf3'         : FmtNetcdf = 'CLASSIC'
96if myargs.fmt == '64bit'           : FmtNetcdf = 'NETCDF3_64BIT_OFFSET'
97if myargs.fmt == '64bit_data'      : FmtNetcdf = 'NETCDF3_64BIT_DATA'
98if myargs.fmt == '64bit_offset'    : FmtNetcdf = 'NETCDF3_64BIT_OFFSET'
99if myargs.fmt == 'netcdf4'         : FmtNetcdf = 'NETCDF4'
100if myargs.fmt == 'netcdf4_classic' : FmtNetcdf = 'NETCDF4_CLASSIC'
101
102#
103if atm_Name.find('LMD') >= 0 : atm_n = 'lmd' ; atmDomainType = 'rectilinear'
104if atm_Name.find('ICO') >= 0 : atm_n = 'ico' ; atmDomainType = 'unstructured'
105
106print ('atmQuantity : ' + str (myargs.atmQuantity) )
107print ('oceQuantity : ' + str (myargs.oceQuantity) )
108
109# Ocean grid periodicity
110oce_perio = myargs.ocePerio
111
112### Read coordinates of all models
113###
114
115diaFile    = xr.open_dataset ( o2a   )
116gridFile   = xr.open_dataset ( grids )
117areaFile   = xr.open_dataset ( areas )
118maskFile   = xr.open_dataset ( masks )
119
120o2aFrac             = diaFile ['OceFrac'].squeeze()
121o2aFrac = np.where ( np.abs(o2aFrac) < 1E10, o2aFrac, 0.0)
122
123(atm_nvertex, atm_jpj, atm_jpi) = gridFile['t'+atm_n+'.clo'][:].shape
124atm_grid_size = atm_jpj*atm_jpi
125atm_grid_rank = len(gridFile['t'+atm_n+'.lat'][:].shape)
126
127atm_grid_center_lat = gridFile['t'+atm_n+'.lat'].squeeze()
128atm_grid_center_lon = gridFile['t'+atm_n+'.lon'].squeeze()
129atm_grid_corner_lat = gridFile['t'+atm_n+'.cla'].squeeze()
130atm_grid_corner_lon = gridFile['t'+atm_n+'.clo'].squeeze()
131atm_grid_area       = areaFile['t'+atm_n+'.srf'].squeeze()
132atm_grid_imask      = 1-maskFile['t'+atm_n+'.msk'][:].squeeze()
133atm_grid_dims       = gridFile['t'+atm_n+'.lat'][:].shape
134
135if atmDomainType == 'unstructured' :
136    atm_grid_center_lat = atm_grid_center_lat.rename ({'ycell':'cell'})
137    atm_grid_center_lon = atm_grid_center_lon.rename ({'ycell':'cell'})
138    atm_grid_corner_lat = atm_grid_corner_lat.rename ({'ycell':'cell'})
139    atm_grid_corner_lon = atm_grid_corner_lon.rename ({'ycell':'cell'})
140    atm_grid_area       = atm_grid_area.rename  ({'ycell':'cell'})
141    atm_grid_imask      = atm_grid_imask.rename ({'ycell':'cell'})
142   
143if atmDomainType == 'rectilinear' :
144    atm_grid_center_lat = atm_grid_center_lat.stack (cell=['y', 'x'])
145    atm_grid_center_lon = atm_grid_center_lon.stack (cell=['y', 'x'])
146    atm_grid_corner_lat = atm_grid_corner_lat.stack (cell=['y', 'x']).rename({'nvertex_lmd':'nvertex'})
147    atm_grid_corner_lon = atm_grid_corner_lon.stack (cell=['y', 'x']).rename({'nvertex_lmd':'nvertex'})
148    atm_grid_area       = atm_grid_area.stack       (cell=['y', 'x'])
149    atm_grid_imask      = atm_grid_imask.stack      (cell=['y', 'x'])
150
151atm_perio = 0
152atm_grid_pmask = atm_grid_imask
153atm_address = np.arange(atm_jpj*atm_jpi)
154
155(oce_nvertex, oce_jpj, oce_jpi) = gridFile['torc.cla'][:].shape ; jpon=oce_jpj*oce_jpj
156oce_grid_size = oce_jpj*oce_jpi
157oce_grid_rank = len(gridFile['torc.lat'][:].shape)
158
159oce_grid_center_lat = gridFile['torc.lat'].stack(oce_grid_size=['y_grid_T', 'x_grid_T'])
160oce_grid_center_lon = gridFile['torc.lon'].stack(oce_grid_size=['y_grid_T', 'x_grid_T'])
161oce_grid_corner_lat = gridFile['torc.cla'].squeeze().stack(oce_grid_size=['y_grid_T', 'x_grid_T'])
162oce_grid_corner_lon = gridFile['torc.clo'].squeeze().stack(oce_grid_size=['y_grid_T', 'x_grid_T'])
163oce_grid_area       = areaFile['torc.srf'].stack(oce_grid_size=['y_grid_T', 'x_grid_T'])
164oce_grid_imask      = 1-maskFile['torc.msk'].stack(oce_grid_size=['y_grid_T', 'x_grid_T'])
165oce_grid_dims       = gridFile['torc.lat'][:].shape
166
167if oce_perio == 0 :
168    if oce_jpi ==  182 : oce_perio = 4 # ORCA 2
169    if oce_jpi ==  362 : oce_perio = 6 # ORCA 1
170    if oce_jpi == 1442 : oce_perio = 6 # ORCA 025
171       
172print ("Oce NPERIO parameter : {:}".format(oce_perio))
173oce_grid_pmask = nemo.lbc_mask (np.reshape(oce_grid_imask.values, (oce_jpj,oce_jpi)), nperio=oce_perio, cd_type='T', sval=0).ravel()
174oce_address = np.arange(oce_jpj*oce_jpi)
175
176print ("Fill closed sea with image processing library")
177oce_grid_imask2D = np.reshape(oce_grid_pmask,(oce_jpj,oce_jpi))
178oce_grid_imask2D = nemo.lbc_mask ( 1-ndimage.binary_fill_holes (1-nemo.lbc(oce_grid_imask2D, nperio=oce_perio, cd_type='T')),
179                                       nperio=oce_perio, cd_type='T', sval=0 )
180oce_grid_imask = oce_grid_imask2D.ravel()
181##
182print ("Computes an ocean coastal band")
183
184oceLand2D  = np.reshape ( np.where (oce_grid_pmask < 0.5, True, False), (oce_jpj, oce_jpi) )
185oceOcean2D = np.reshape ( np.where (oce_grid_pmask > 0.5, True, False), (oce_jpj, oce_jpi) )
186
187NNocean = 1+2*oceCoastWidth
188oceOceanFiltered2D = ndimage.uniform_filter(oceOcean2D.astype(float), size=NNocean)
189oceCoast2D = np.where (oceOceanFiltered2D<(1.0-0.5/(NNocean**2)),True,False) & oceOcean2D
190oceCoast2D = nemo.lbc_mask (np.reshape(oceCoast2D,(oce_jpj,oce_jpi)), nperio=oce_perio, cd_type='T').ravel()
191
192oceOceanFiltered = oceOceanFiltered2D.ravel()
193oceLand  = oceLand2D.ravel ()
194oceOcean = oceOcean2D.ravel()
195oceCoast = oceCoast2D.ravel()
196
197print ('Number of points in oceLand  : {:8d}'.format (oceLand.sum())  )
198print ('Number of points in oceOcean : {:8d}'.format (oceOcean.sum()) )
199print ('Number of points in oceCoast : {:8d}'.format (oceCoast.sum()) )
200
201# Arrays with coastal points only
202oceCoast_grid_center_lon = oce_grid_center_lon[oceCoast]
203oceCoast_grid_center_lat = oce_grid_center_lat[oceCoast]
204oceCoast_grid_area       = oce_grid_area      [oceCoast]
205oceCoast_grid_imask      = oce_grid_imask     [oceCoast]
206oceCoast_grid_pmask      = oce_grid_pmask     [oceCoast]
207oceCoast_address         = oce_address        [oceCoast]
208
209print ("Computes an atmosphere coastal band " )
210atmLand      = np.where (o2aFrac[:] < epsfrac       , True, False)
211atmLandFrac  = np.where (o2aFrac[:] < zone-epsfrac  , True, False)
212atmFrac      = np.where (o2aFrac[:] > epsfrac       , True, False) & np.where (o2aFrac[:] < (zone-epsfrac), True, False)
213atmOcean     = np.where (o2aFrac[:] < (zone-epsfrac), True, False)
214atmOceanFrac = np.where (o2aFrac[:] > epsfrac       , True, False)
215
216## For LMDZ only !!
217if atmDomainType == 'rectilinear' :
218    print ("Extend coastal band " )
219    NNatm = 1+2*atmCoastWidth
220    atmLand2D = np.reshape ( atmLand, ( atm_jpj, atm_jpi) )
221
222    atmLandFiltered2D = ndimage.uniform_filter(atmLand2D.astype(float), size=NNatm)
223    atmCoast2D = np.where (atmLandFiltered2D<(1.0-0.5/(NNatm**2)),True,False) & atmLandFrac
224   
225    atmLandFiltered = atmLandFiltered2D.ravel()
226    atmCoast = atmCoast2D.ravel()
227
228    print ('Number of points in atmLand  : {:8d}'.format (atmLand.sum())  )
229    print ('Number of points in atmOcean : {:8d}'.format (atmOcean.sum()) )
230    print ('Number of points in atmCoast : {:8d}'.format (atmCoast.sum()) )
231
232else :
233    atmCoast = atmFrac
234   
235   
236# Arrays with coastal points only
237atmCoast_grid_center_lon = atm_grid_center_lon[atmCoast]
238atmCoast_grid_center_lat = atm_grid_center_lat[atmCoast]
239atmCoast_grid_area       = atm_grid_area      [atmCoast]
240atmCoast_grid_imask      = atm_grid_imask     [atmCoast]
241atmCoast_grid_pmask      = atm_grid_pmask     [atmCoast]
242atmCoast_address         = atm_address        [atmCoast]
243
244# Initialisations before the loop
245remap_matrix = np.empty ( shape=(0), dtype=np.float64 )
246atm_address  = np.empty ( shape=(0), dtype=np.int32   )
247oce_address  = np.empty ( shape=(0), dtype=np.int32   )
248
249## Loop on atmosphere coastal points
250if searchRadius > 0. :
251    print ("Loop on atmosphere coastal points")
252    for ja in np.arange(len(atmCoast_grid_pmask)) :
253        z_dist = geodist ( atmCoast_grid_center_lon[ja], atmCoast_grid_center_lat[ja], oceCoast_grid_center_lon, oceCoast_grid_center_lat)
254        z_mask = np.where (z_dist*ra < searchRadius, True, False)
255        num_links = int(z_mask.sum())
256        if num_links == 0 : continue
257        z_area = oceCoast_grid_area[z_mask].sum().values
258        poids = np.ones ((num_links),dtype=np.float64) / z_area
259        if myargs.atmQuantity == 'Surfacic' : poids = poids * atm_grid_area[ja]
260        if myargs.oceQuantity == 'Quantity' : poids = poids * oceCoast_grid_area[z_mask]
261        if  ja % (len(atmCoast_grid_pmask)//50) == 0 : # Control print
262            print ( 'ja:{:8d}, num_links:{:8d},  z_area:{:8.4e},  atm area:{:8.4e},  weights sum:{:8.4e}  '
263                        .format(ja, num_links, z_area, atm_grid_area[ja].values, poids.sum() ) )
264        #
265        matrix_local = poids
266        atm_address_local = np.ones(num_links, dtype=np.int32 ) * atmCoast_address[ja]
267        # Address on destination grid
268        oce_address_local = oceCoast_address[z_mask]
269        # Append to global arrays
270        remap_matrix = np.append ( remap_matrix, matrix_local      )
271        atm_address  = np.append ( atm_address , atm_address_local )
272        oce_address  = np.append ( oce_address , oce_address_local )
273
274    print ('End of loop')
275
276num_links = remap_matrix.shape[0]
277
278print ("Write output file")
279runoff = myargs.output
280print ('Output file: ' + runoff )
281
282
283remap_matrix = xr.DataArray ( np.reshape(remap_matrix, (num_links, 1)), dims = ['num_links', 'num_wgts'] )
284
285# OASIS uses Fortran style indexing, starting at one
286src_address  = xr.DataArray ( atm_address.astype(np.int32)+1, dims = ['num_links'],
287                                 attrs={"convention": "Fortran style addressing, starting at 1"}) 
288dst_address  = xr.DataArray ( oce_address.astype(np.int32)+1, dims = ['num_links'],
289                                 attrs={"convention": "Fortran style addressing, starting at 1"})
290
291src_grid_dims       = xr.DataArray (np.array(atm_grid_dims, dtype=np.int32), dims = ['src_grid_rank',] )
292src_grid_center_lon = xr.DataArray (atm_grid_center_lon.values , dims = ['src_grid_size',] )
293src_grid_center_lat = xr.DataArray (atm_grid_center_lat.values , dims = ['src_grid_size',] )
294
295src_grid_center_lon.attrs['units']='degrees_east'  ; src_grid_center_lon.attrs['long_name']='Longitude'
296src_grid_center_lon.attrs['long_name']='longitude' ; src_grid_center_lon.attrs['bounds']="src_grid_corner_lon"
297src_grid_center_lat.attrs['units']='degrees_north' ; src_grid_center_lat.attrs['long_name']='Latitude'
298src_grid_center_lat.attrs['long_name']='latitude ' ; src_grid_center_lat.attrs['bounds']="src_grid_corner_lat"
299 
300src_grid_corner_lon = xr.DataArray (atm_grid_corner_lon.values.transpose(), dims = [ 'src_grid_size', 'src_grid_corners'] )
301src_grid_corner_lat = xr.DataArray (atm_grid_corner_lat.values.transpose(), dims = [ 'src_grid_size', 'src_grid_corners'] )
302src_grid_corner_lon.attrs['units']="degrees_east"
303src_grid_corner_lat.attrs['units']="degrees_north"
304
305src_grid_area       =  xr.DataArray (atm_grid_area.values, dims = ['src_grid_size',] ) 
306src_grid_area.attrs['long_name']="Grid area" ; src_grid_area.attrs['standard_name']="cell_area" ; src_grid_area.attrs['units']="m2"
307
308src_grid_imask      =  xr.DataArray (atm_grid_imask.values, dims = ['src_grid_size',] ) 
309src_grid_imask.attrs['long_name']="Land-sea mask" ; src_grid_imask.attrs['units']="Land:1, Ocean:0"
310
311src_grid_pmask      =  xr.DataArray (atm_grid_pmask.values, dims = ['src_grid_size',] ) 
312src_grid_pmask.attrs['long_name']="Land-sea mask (periodicity removed)" ; src_grid_pmask.attrs['units']="Land:1, Ocean:0"
313
314# --
315dst_grid_dims       = xr.DataArray (np.array(oce_grid_dims, dtype=np.int32), dims = ['dst_grid_rank',] )
316dst_grid_center_lon = xr.DataArray (oce_grid_center_lon.values, dims = ['dst_grid_size',] )
317dst_grid_center_lat = xr.DataArray (oce_grid_center_lat.values, dims = ['dst_grid_size',] )
318
319dst_grid_center_lon.attrs['units']='degrees_east'  ; dst_grid_center_lon.attrs['long_name']='Longitude'
320dst_grid_center_lon.attrs['long_name']='longitude' ; dst_grid_center_lon.attrs['bounds']="dst_grid_corner_lon"
321dst_grid_center_lat.attrs['units']='degrees_north' ; dst_grid_center_lat.attrs['long_name']='Latitude'
322dst_grid_center_lat.attrs['long_name']='latitude ' ; dst_grid_center_lat.attrs['bounds']="dst_grid_corner_lat"
323
324dst_grid_corner_lon = xr.DataArray (np.transpose(oce_grid_corner_lon.values), dims = [ 'dst_grid_size', 'dst_grid_corners'] )
325dst_grid_corner_lat = xr.DataArray (np.transpose(oce_grid_corner_lat.values), dims = [ 'dst_grid_size', 'dst_grid_corners'] )
326dst_grid_corner_lon.attrs['units']="degrees_east"
327dst_grid_corner_lat.attrs['units']="degrees_north"
328
329dst_grid_area       =  xr.DataArray (oce_grid_area.values, dims = ['dst_grid_size',] ) 
330dst_grid_area.attrs['long_name']="Grid area" ; dst_grid_area.attrs['standard_name']="cell_area" ; dst_grid_area.attrs['units']="m2"
331
332dst_grid_imask      =  xr.DataArray (oce_grid_imask.astype(np.int32), dims = ['dst_grid_size',] ) 
333dst_grid_imask.attrs['long_name']="Land-sea mask" ; dst_grid_imask.attrs['units']="Land:1, Ocean:0"
334
335dst_grid_pmask      =  xr.DataArray (oce_grid_pmask, dims = ['dst_grid_size',] ) 
336dst_grid_pmask.attrs['long_name']="Land-sea mask (periodicity removed)" ; dst_grid_pmask.attrs['units']="Land:1, Ocean:0"
337
338src_lon_addressed   =  xr.DataArray (atm_grid_center_lon.values[atm_address]                  , dims = ['num_links'] )
339src_lat_addressed   =  xr.DataArray (atm_grid_center_lat.values[atm_address]                  , dims = ['num_links'] )
340src_area_addressed  =  xr.DataArray (atm_grid_area      .values[atm_address]                  , dims = ['num_links'] )
341src_imask_addressed =  xr.DataArray (1-atm_grid_imask   .values[atm_address].astype(np.int32) , dims = ['num_links'] )
342src_pmask_addressed =  xr.DataArray (1-atm_grid_pmask   .values[atm_address].astype(np.int32) , dims = ['num_links'] )
343
344dst_lon_addressed   =  xr.DataArray (oce_grid_center_lon.values[atm_address], dims = ['num_links'] )
345dst_lat_addressed   =  xr.DataArray (oce_grid_center_lat.values[oce_address], dims = ['num_links'] )
346dst_area_addressed  =  xr.DataArray (oce_grid_area.values[oce_address].astype(np.int32)      , dims = ['num_links'] )
347dst_imask_addressed =  xr.DataArray (1-oce_grid_imask[oce_address].astype(np.int32)   , dims = ['num_links'] )
348dst_pmask_addressed =  xr.DataArray (1-oce_grid_pmask[oce_address].astype(np.int32)   , dims = ['num_links'] )
349
350src_lon_addressed.attrs['long_name']="Longitude" ; src_lon_addressed.attrs['standard_name']="longitude" ; src_lon_addressed.attrs['units']="degrees_east"
351src_lat_addressed.attrs['long_name']="Latitude"  ; src_lat_addressed.attrs['standard_name']="latitude"  ; src_lat_addressed.attrs['units']="degrees_north"
352
353dst_lon_addressed.attrs['long_name']="Longitude" ; dst_lon_addressed.attrs['standard_name']="longitude" ; dst_lon_addressed.attrs['units']="degrees_east"
354dst_lat_addressed.attrs['long_name']="Latitude"  ; dst_lat_addressed.attrs['standard_name']="latitude"  ; dst_lat_addressed.attrs['units']="degrees_north"
355
356if atmDomainType == 'rectilinear' :
357    atmLand         = xr.DataArray ( atmLand.ravel()     , dims = ['src_grid_size',] )
358    atmLandFiltered = xr.DataArray ( atmLandFrac.ravel() , dims = ['src_grid_size',] )
359    atmLandFrac     = xr.DataArray ( atmFrac.ravel()     , dims = ['src_grid_size',] )
360    atmFrac         = xr.DataArray ( atmFrac.ravel()     , dims = ['src_grid_size',] )
361    atmOcean        = xr.DataArray ( atmOcean.ravel()    , dims = ['src_grid_size',] )
362    atmOceanFrac    = xr.DataArray ( atmOceanFrac.ravel(), dims = ['src_grid_size',] )
363
364atmCoast         = xr.DataArray (atmCoast.astype(np.int32)          , dims = ['src_grid_size',])
365oceLand          = xr.DataArray (oceLand.astype(np.int32)           , dims = ['dst_grid_size',])
366oceOcean         = xr.DataArray (oceOcean.astype(np.int32)          , dims = ['dst_grid_size',])
367oceOceanFiltered = xr.DataArray (oceOceanFiltered.astype(np.float32), dims = ['dst_grid_size',])
368oceCoast         = xr.DataArray (oceCoast.astype(np.int32)          , dims = ['dst_grid_size',])
369
370
371f_runoff = xr.Dataset ( {
372    'remap_matrix'            : remap_matrix,
373        'src_address'         : src_address,
374        'dst_address'         : dst_address,
375        'src_grid_dims'       : src_grid_dims,
376        'src_grid_center_lon' : src_grid_center_lon,
377        'src_grid_center_lat' : src_grid_center_lat,
378    'src_grid_corner_lon' : src_grid_corner_lon,
379    'src_grid_corner_lat' : src_grid_corner_lat,
380        'src_grid_area'       : src_grid_area,
381        'src_grid_area'       : src_grid_area,
382        'src_grid_pmask'      : src_grid_pmask,
383        'dst_grid_dims'       : dst_grid_dims,
384        'dst_grid_center_lon' : dst_grid_center_lon,
385        'st_grid_center_lat'  : dst_grid_center_lat,
386        'dst_grid_corner_lon' : dst_grid_corner_lon,
387        'dst_grid_corner_lat' : dst_grid_corner_lat,
388        'dst_grid_area'       : dst_grid_area,
389        'dst_grid_imask'      : dst_grid_imask,
390        'dst_grid_pmask'      : dst_grid_pmask,
391        'src_lon_addressed'   : src_lon_addressed,
392        'src_lat_addressed'   : src_lat_addressed,
393        'src_area_addressed'  : src_area_addressed,
394        'dst_lon_addressed'   : dst_lon_addressed,
395        'dst_lat_addressed'   : dst_lat_addressed,
396        'dst_area_addressed'  : dst_area_addressed,
397        'dst_imask_addressed' : dst_imask_addressed,
398        'dst_pmask_addressed' : dst_pmask_addressed,
399        'atmCoast'            : atmCoast,
400        'oceLand'             : oceLand,
401        'oceOcean'            : oceOcean,
402        'oceOceanFiltered'    : oceOceanFiltered,
403        'oceCoast'            : oceCoast
404    } )
405
406f_runoff.attrs['Conventions']     = "CF-1.6"
407f_runoff.attrs['source']          = "IPSL Earth system model"
408f_runoff.attrs['group']           = "ICMC IPSL Climate Modelling Center"
409f_runoff.attrs['Institution']     = "IPSL https.//www.ipsl.fr"
410f_runoff.attrs['Ocean']           = oce_Name + " https://www.nemo-ocean.eu"
411f_runoff.attrs['Atmosphere']      = atm_Name + " http://lmdz.lmd.jussieu.fr"
412f_runoff.attrs['associatedFiles'] = grids + " " + areas + " " + masks
413f_runoff.attrs['description']     = "Generated with RunoffWeights.py"
414f_runoff.attrs['title']           = runoff
415f_runoff.attrs['Program']         = "Generated by " + sys.argv[0] + " with flags " + ' '.join (sys.argv[1:]) 
416f_runoff.attrs['atmCoastWidth']   = "{:d} grid points".format(atmCoastWidth)
417f_runoff.attrs['oceCoastWidth']   = "{:d} grid points".format(oceCoastWidth)
418f_runoff.attrs['searchRadius']    = "{:.0f} km".format(searchRadius/1000.)
419f_runoff.attrs['atmQuantity']     = myargs.atmQuantity
420f_runoff.attrs['oceQuantity']     = myargs.oceQuantity
421f_runoff.attrs['gridsFile']       = grids
422f_runoff.attrs['areasFile']       = areas
423f_runoff.attrs['masksFile']       = masks
424f_runoff.attrs['o2aFile']         = o2a
425f_runoff.attrs['timeStamp']       = time.asctime ()
426try    : f_calving.attrs['directory'] = os.getcwd ()
427except : pass
428try    : f_runoff.attrs['HOSTNAME'] = platform.node ()
429except : pass
430try    : f_runoff.attrs['LOGNAME']  = os.getlogin ()
431except : pass
432try    : f_runoff.attrs['Python']   = "Python version " +  platform.python_version ()
433except : pass
434try    : f_runoff.attrs['OS']       = platform.system ()
435except : pass
436try    : f_runoff.attrs['release']  = platform.release ()
437except : pass
438try    : f_runoff.attrs['hardware'] = platform.machine ()
439except : pass
440f_runoff.attrs['conventions']     = "SCRIP"
441f_runoff.attrs['source_grid']     = "curvilinear"
442f_runoff.attrs['dest_grid']       = "curvilinear"
443f_runoff.attrs['Model']           = "IPSL CM6"
444f_runoff.attrs['SVN_Author']      = "$Author$"
445f_runoff.attrs['SVN_Date']        = "$Date$"
446f_runoff.attrs['SVN_Revision']    = "$Revision$"
447f_runoff.attrs['SVN_Id']          = "$Id$"
448f_runoff.attrs['SVN_HeadURL']     = "$HeadURL$"
449
450f_runoff.to_netcdf ( runoff, mode='w', format=FmtNetcdf )
451f_runoff.close ()
452
453##
454
455print ('That''s all folks !')
456## ======================================================================================
Note: See TracBrowser for help on using the repository browser.